Large-scale analyses of brain functional connectivity can uncover disruptions in the activity and connectivity of various brain regions, which may lead to neurological disorders. In this talk, I will present our study that employs persistent homology —a prominent tool in topological data analysis (TDA)— to investigate changes in resting-state functional connectivity in two contexts: healthy aging and autism spectrum disorder (ASD). Specifically, we focus on functional connectivity changes across three distinct scales: (a) Global Scale (brain-wide), (b) Mesoscopic Scale (resting-state network level), and (c) Local Scale (region level). We have developed an innovative persistent homology-based method, namely node persistence, to identify brain regions that exhibit significant differences between the groups under investigation. Finally, we systematically compare these regions, which display notable between-group differences as highlighted by topological local measures, with clinically relevant regions identified in previously published non-invasive brain stimulation (NIBS) studies.